IEEE International Interconnect Technology Conference (IITC) 2024

The 27th IITC, sponsored by IEEE Electron Devices Society, is the premier conference for interconnect technology focused on advanced metallization and 3D integration for ULSI IC applications.

Type of Event


Our Participation


03 Jun 2024 - 06 Jun 2024

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Our Participation

Wednesday, June 5

Poster Session & Conference Reception from 17:30-20:00

  • P11. Data Analytics to Identify Improved Low k Films | Authors: William Entley, Achtyl Jennifer, Robert Ridgeway

This paper investigates the development of as deposited dense low k films with high mechanical strength, crucial for advanced semiconductor manufacturing. Over 1100 unique low k films from seventeen precursors were deposited and analyzed, focusing on mechanical properties. The study used a Design of Experiments (DOE) approach to maximize mechanical strength of each precursor. Data analytics were used to analyze and sort the low k films to identify precursors that had the highest mechanical strength at a given value of the dielectric constant. 

This research highlights the potential of tailored precursor design to meet the demanding requirements of dense low k films in advanced semiconductor nodes.

  • P12. Halogen Free Low Temperature ALD Mo-based Films for Interconnects Applications | Authors: Aein Babadi, Randall Higuchi, Mike Savo, Charlene Chen, Bhushan Zope, Sergei Ivanov 

Molybdenum-based films synthesized by low temperature atomic layer deposition (ALD) from different organometallic halogen free Molybdenum precursors. The film's average composition was investigated and correlated with their nanoscale structures and electrical transport properties. A high work function N-rich MoN film with low carbon contamination was demonstrated in comparison with MoC/MoCN films with low resistivity. The precursor composition dependence of film’s properties leads to a low resistivity MoC film as well as selective deposition of Mo films on metallic substrates. 

  • P19. 300mm Wafer-scale ALD-grown MoS2 for Cu diffusion barrier | Authors: Thong Ngo, Angelica Zacatzi, Yuanqiu Tan, Daniel Lee, Anand Wankis, Nguyen Vu, Ravi Kanjolia, Mansour Moinpour

Cu has served as the interconnect metal in various chip generations, typically accompanied by a TaN/Ta diffusion barrier/liner. The size-scaling demand for future nodes requires thinning down TaN/Ta bi-layer. The thickness of TaN/Ta has reached the limit of its barrier/liner capability, necessitating the exploration of alternative diffusion barrier/liner materials. In this paper, we report the 300mm wafer-scale atomic layer deposited MoS2 as a promising candidate for Cu diffusion barrier/liner interconnect. MoS2 was deposited on thermal SiO2 substrate at the temperatures ranging from 200-550 °C. Good conformality of MoS2 was demonstrated on high aspect ratio structure. The resistivity of Cu/MoS2/SiO2 stack is lower than that of Cu/SiO2 stack. Time dependent dielectric breakdown of Cu/MoS2/SiO2 will be discussed and benchmarked with Cu/Ta/TaN/SiO2.

Our Participation

Wednesday, June 6

Technical Presentation from 14:50-15:10

  • 13.3 Ligand Engineering to Machine learning: Optimizing Ru ALD for Ultrathin Film Deposition | Authors: Jay Chiu, Isiah Liu, Chang-Won Lee, Guo Liu, Bhushan Zop

This study demonstrated our significant advancements in H2-based thermal Ru ALD, using novel Ru precursors, RuEM8 and RuEM10, which enable fabrication of ultra-thin Ru films with: excellent continuity at thicknesses as low as 3 nm, over 99% conformality, greater than 99 wt.% purity, and bulk resistivity reduced to 15 uΩ·cm. Such attributes position them as prime candidates for interconnect applications. Those breakthroughs were built on our strategic innovations: (1) A Machine-learning guided DoE methodology for high-efficiency ALD process optimization. (2) Smoothing-agent post-treatment to improve continuity via modulating Ru 26 surface diffusion. (3) tailored ligands as "in-situ" inhibitors to facilitate 2D Ru growth. This showcased the combination of chemistry domain knowledge and machine learning forms the basis of a high-efficiency ALD precursor development methodology.

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